class_docstr: Evaluate metrics for y_true and y_pred.
code: def get_metric_mode(
      metric: Union[Callable[..., Any], str, None]
  ) -> str:
      Evaluator.check_metric(metric)
      if metric in Evaluator.max_mode_metrics:
          return "max"
      else:
          return "min"
  
imports: from sklearn.metrics import mean_squared_error
  from sklearn.metrics import r2_score
  from sklearn.metrics import mean_absolute_error
  from sklearn.metrics import mean_squared_log_error
  import numpy
  import pandas
  from bigdl.dllib.utils.log4Error import *
  from bigdl.dllib.utils.log4Error import invalidInputError
  from numpy import float64, ndarray
  from typing import List, Tuple, Union, Optional, Callable, Any
label_desc: Use image node_id 3 for calling the Evaluator obj's underlying member method code with example usage: obj.get_metric_mode(metric) and returns: str, str